Identifying the Hidden Costs of a Public Health Success: Arsenic Well Water Contamination and ...

77 Pages·2013·0.43 MB·English

Identifying the Hidden Costs of a Public Health Success:
Arsenic Well Water Contamination and Productivity in Bangladesh
Mark M. Pitt
Brown University
Mark R. Rosenzweig
Yale University
Nazmul Hassan
University of Dhaka
May 2013
The research reported in this paper was supported by grants from the National Institutes of Health,
ROlDK072413, the International Growth Center, RA-2009-ll-028, the William and Flora Hewlett
Foundation, 5-29193, and from the MacMillan Center, Yale University. We are grateful to Katherine
Kelley of the University of Rhode Island and Dave Murray and Bob Correra of Brown University
for their excellent lab work, to Eva Kolker for research assistance and to Pascaline Dupas for
insightful comments on an earlier draft.
Abstract

We exploit recent molecular genetics evidence on the genetic basis of arsenic excretion and unique
information on family links among respondents living in different environments from a large panel survey
within a theoretical framework incorporating optimizing behavior to uncover the hidden costs of arsenic
poisoning in Bangladesh. We provide for the first time estimates of the effects of the ingestion and retention
of inorganic arsenic on direct measures of cognitive and physical capabilities as well as on the schooling
attainment, occupational structure, entrepreneurship and incomes of the rural Bangladesh population. We
also provide new estimates of the effects of the consumption of foods grown and cooked in arsenic-
contaminated water on individual arsenic concentrations. The estimates are based on arsenic biomarkers
obtained from a sample of members of rural households in Bangladesh who are participants in a long-term
panel survey following respondents and their coresident household members over a period of 26 years.
1. Introduction
In the 1970s and 1980s the government of Bangladesh, with the support and financing of
the United Nations Children’s Fund, promoted the digging of tube wells to provide clean drinking
water and reduce the incidence of diarrheal disease. Prior to this, drinking water came from surface
water sources, which were identified as a principal source of diarrheal disease. This shift in water
sources has been attributed to having played a major role in the reductions in morbidity experienced
in Bangladesh, particularly with respect to water-born diseases. Indeed, despite little growth in rural
wages and no change in average calorie intake (Pitt et al., 2012), there has been significant increases
in average BMI by age and in height for the rural population of Bangladesh, as shown in Appendix
Figures A and B for the period 1982 and 2008 based on panel survey data describing a
representative population of rural households in 1982.
In the late 1990s, however, evidence indicated that groundwater, unlike surface water and by
then the main source of water for drinking, irrigation and cooking in Bangladesh, was contaminated
by naturally-occurring arsenic in 59 of the country’s 64 districts. The contamination of groundwater
by arsenic in Bangladesh is the largest poisoning of a population in history (Smith et al., 2000). As
95% of the country's population relies on well water, an estimated 57 million people have been
chronically exposed to drinking water with arsenic levels exceeding the WHO standard of 10 ìg/L,
and 35 million people were exposed to arsenic levels above the country's government standard of 50
ìg/L (British Geological Survey, 1999).
While there is now a large body of evidence that the consumption of arsenic-contaminated
water is manifested in new diseases and symptoms, mostly related to skin lesions, there is little
evidence on the economic consequences of arsenic ingestion and retention in the body in
Bangladesh. Unlike for diseases such as dysentery and diarrhea, the debilitating effects of arsenic on
productivity are not conspicuous, are largely hidden, if they exist. Such effects are also difficult to
estimate, given that arsenic levels in humans reflect consumption and time-allocation choices and
much of the population does not work for wages. In this paper we exploit recent molecular genetics
evidence on the genetic basis of arsenic excretion and unique information on family links among
respondents living in different environments from a large panel survey within a theoretical
framework incorporating optimizing behavior to uncover the hidden costs of arsenic poisoning in
Bangladesh. In particular, we provide new estimates of the effects of the ingestion and retention of
inorganic arsenic on direct measures of cognitive and physical capabilities as well as on the schooling
attainment, occupational structure, entrepreneurship and incomes of the rural Bangladesh
population. We also provide new estimates of the effects of the consumption of foods grown and
cooked in arsenic-contaminated water on individual arsenic concentrations.
The estimates are based on arsenic biomarkers obtained from a sample of members of rural
households in Bangladesh who are participants in a long-term panel survey following respondents
and their coresident household members over a period of 26 years. In the last round of the survey
each respondent in the survey aged eight years and above provided clippings from all ten toenails.
Toenail clippings are the preferred biomarkers for measurements of arsenic ingestion in
environments in which exposure lasts more than a few months and where a significant share of
arsenic exposure occurs through food consumption. Our new estimates of the causal impacts of
arsenic exploit the new biomedical evidence on genetic linkages among kin in the ability to methylate
arsenic, thus reducing its toxic effects, and data identifying family linkages among respondents living
in separate environments (villages).
A large number of studies have related arsenic exposure in the environment, measured by
well contamination, and diet to biomarkers for arsenic in humans (urine, blood, hair, and toenails)
and to specific health outcomes. However, these studies have a number of deficiencies. Neither
proximity to arsenic sources (wells) nor the ingestion of arsenic can be considered exogenous to the
outcomes studied. First, especially given campaigns to identify wells that are contaminated,
households can choose to avoid arsenic contamination by choosing surface-water sources for
cooking and drinking that are less convenient, perhaps then risking the incidence of water-born
disease (Fields et al., 2010). Second, arsenic ingestion and retention are related to the nutrient
composition of foods, which also reflect preferences and income constraints. Estimates of the
relative importance of drinking water and food consumption as sources of arsenic, based on the
distribution of arsenic among wells in Bangladesh and the non-linear relationship between arsenic
ingestion and exposure levels, suggest that dietary sources of arsenic are the most important route of
arsenic exposure for two-thirds of the Bangladesh population (Kile et al. 2007). Moreover, specific
foods differ in the degree to which they retain arsenic used in their cultivation and cooking and
differ as well in the extent to which, via their nutritional composition, they enable the metabolism
(detoxification) and subsequent excretion of arsenic, primarily through a process of methylation.1
1For example, Zablotska et al. (2008) and Heck et al. (2007), based on randomized field
experiments, highlight the protective effects of folate-related nutrients and B-vitamins in samples
2
An individual's retained arsenic in a country such as Bangladesh, where millions of wells
have been tested and identified by their levels of arsenic contamination and where diet is both a
source of arsenic and an aid to its metabolism, is clearly not an exogenous variable. But studies that
carry out randomized nutrient supplementation that can reduce arsenic’s burden by increasing the
rate at which it is metabolized (methylated) do not identify the mechanisms by which such arsenic-
reducing interventions improve health. Such supplements improve health and thus productivity by
more than just reducing the toxic concentration of arsenic and its metabolites in body tissue.
There is little evidence on the effects of arsenic contamination on performance measures,
and none on economic outcomes such as earnings or productivity.2 In recent years, however, the
specific set of inheritable genes associated with the abilities of humans to metabolize and secrete
ingested arsenic have been identified, with field studies showing that retained arsenic levels are
correlated within families consistent with genetic models. Genetic variation in abilities to methylate
arsenic is thus an important component of the large variance in arsenic found in humans.
Importantly, recent studies have shown, and we present additional evidence, that the genes
associated with arsenic retention in Bangladesh do not predict measures of cognition or
anthropometric outcomes in populations not exposed to arsenic. Variation in these genes across
families thus provides a basis for the identification of the causal effects of arsenic retention on such
outcomes where arsenic is present in the diet. This linkage, however, has not been exploited in
studies examining the consequences of arsenic. Knowledge of both how arsenic retention directly
and causally affects health and productivity outcomes and how diet affects arsenic retention is
obviously critical to any cost-benefit analyses of governmental programs aimed at improving health
and productivity.
In section 2 of the paper we set out a simple model that highlights the distinct endogenous
and exogenous components of an individual’s retained arsenic. We show that under realistic
from rural Bangladesh. Leafy vegetables are known to contain high concentration of folic acid.
However, Hossain (2006), based on extensive tests in Bangladesh, reports that among the food
products tested, leafy vegetables have the highest concentration of arsenic.
2Asadullaha et al. (2011) find that proximity to contaminated wells is associated with lower
test scores among school-age children, but identification rests on the assumption that proximity to
water sources is random, and does not take into account that households may select their water
sources endogenously.
3
assumptions about the information set of agents, including that productivity effects of retained
arsenic are hidden but health effects are common knowledge, it is not possible to sign the bias in
correlational relationships between retained arsenic and productivity outcomes due to optimizing
with respect to water choice. Section 3 describes the data and our measure of arsenic retention from
the sample of toenail clippings in our rural sample. The data indicate very high levels of arsenic
concentrations, exceeding average levels measured in US respondents by almost 20 to one, with
concentrations exhibiting wide individual variation but spread almost uniformly across landholding
groups.
In section 4 we discuss how we use information on the measured retained arsenic of family
members residing in different villages and estimates of the effects of food consumption on arsenic
retention and ingestion to identify the causal effects of arsenic retention on a variety of outcomes at
the individual and household level. To implement the identification method, we first obtain
estimates of the causal effects of the individual consumption of seven food groups on arsenic
concentrations based on individual-specific food consumption information, water source choice and
smoking on the toenail-based arsenic measures. These show that diet matters for arsenic
contamination, with the consumption of grains, the staple of the Bangladesh diet, significantly
increasing, but tubers, in accord with the medical literature, reducing measured arsenic
concentrations. We also find that using non tubewell water sources for cooking reduces arsenic
concentrations by 18%.
In the next sections we examine the relationship between retained arsenic and measures of
individual capabilities, schooling, health and economic choices using as instruments measures of
arsenic retention net of the influence of diet, water-source choices, and village fixed effects of
separated family members. We find that OLS estimates significantly understate the negative effects
of retained arsenic on cognition and physical strength. We also assess the validity of our
identification strategy. We first show that the contemporaneous cognitive impairment from arsenic
ingestion is the same for old and young. In contrast, however, we find that the negative effects on
cognition are only manifested in lower schooling attainment and lower likelihoods of being in a skill
occupation or running a nonfarm business for those (young) cohorts of males who were of school
age or who entered the labor force after the switch to arsenic-contaminated tubewells as water
sources. Older cohorts’ schooling attainment and occupation are unaffected. Using the same
methods, we could find no effect of arsenic retention on BMI, height or conventional morbidity
4
symptoms for all age cohorts, consistent with the productivity effects of arsenic contamination being
hidden in the population.
In section 4 we focus on the identification of the effects of arsenic on earnings and
household productivity. The challenge for estimating earnings effects is that in Bangladesh less than
half of the male labor force are wage earners. We extend the individual model to incorporate realistic
features of rural Bangladesh households that shows how it is possible to identify the causal effects of
arsenic ingestion on male earnings using household expenditure data. Given that we have data on
consumption expenditures for all households, we can avoid potential selectivity problems that
plague estimates of earnings effects from improving health based solely on the earnings of wage
workers.
Another feature of Bangladesh households is that almost no women are earners, but women
play a large role in household production. We show, using the model, that it is possible to identify if
arsenic also reduces home productivity and whether home goods are complements or substitutes
with purchased goods by estimating the effects of the retained arsenic of men and women on
households’ choices of water sources. We also show that knowledge of these parameters is necessary
to make inferences about the potential welfare effects of arsenic reduction based on earnings
estimates as well as inferences about the consequences of efforts to provide cleaner water sources.
In the penultimate section, we implement the instrumental-variables method in a multi-
member household context that is consistent with the household model. We show that our data
meet the conditions for identifying the causal effects of retained arsenic by gender on earnings using
household expenditure data. Our estimates indicate that lowering the amount of retained arsenic
among Bangladesh prime-age males to those levels in uncontaminated countries would increase
earnings by 9%. We find that retained arsenic also reduces the productivity of women in home
production, and that home goods and purchased goods are complements. These latter results imply
that the estimated increase in total household expenditures through eliminating arsenic closely
corresponds to the overall consumption benefits to the household. They also imply, however, that
improvements in water quality at the source would be less effective than measures that reduce the
retention of ingested arsenic due to the endogenous response of households in their own efforts to
reduce arsenic ingestion. In the conclusion we summarize our results and attempt to quantify the
productivity benefits from the provision of arsenic-free water to Bangladeshi households.
2. Modeling the Effects of Arsenic Contamination on Individuals
5
In this section we describe a simple one-person model to fix ideas about how heterogeneity
in water quality, preferences, health information, and abilities to methylate ingested arsenic
combined with optimizing behavior affect inferences about the relationship between an individual’s
measured amount of retained arsenic3 and measures of capabilities and economic outcomes. In
particular, in accordance with the medical literature, retained arsenic A for individual i residing in
ij
environment j depends on the consumption of specific foods, where C is a vector of consumed
ijx
foods indexed by x; on the quality (arsenic content) of the water used for cooking and drinking ù ;
ij
and on the individual’s endowed ability to methylate arsenic, given by ì .
ij
(1) A = A(C , ù , ì )
ij ijx ij ij
We assume that A <0; A <0; and A , A , A >0, so that higher ù indicates less contamination by
2 3 22 33 32 ij
arsenic4Consumed water quality depends on the quality of the water source e and the individual’s
. j
purification effort t :
ij
(2) ù = ù(t ) + e ,
ij ij j
where ù(cid:78)>0, ù(cid:79)<0. t may include time spent fetching water from alternative sources, time boiling or
ij
otherwise treating water from water sources that have biological contaminants but lower arsenic,
such as surface water sources, and/or time spent collecting additional fuel needed for water boiling.5
The budget constraint is given by (3)
(3) F + (Ù - t )w = Óp C ,
ij ij ij jl ijx
where F =non-earnings sources of income and the p are local food prices. The individual’s wage
ij jx
rate w depends on the individual’s capability or skill h , which has a local per-unit rental price w, so
ij ij j
that w = wh , and skill is affected negatively by retained arsenic and positively by a skill endowment
ij j ij
3Retained arsenic in this context is the concentration of arsenic in body tissue. The measure
used in the empirical work is the arsenic concentration of toenails, the preferred biomaker of arsenic
retained in human tissue.
4The amount of retained arsenic depends on the quantity of water consumed. For simplicity
we assume that this is constant across individuals of given age and gender. Note that A cannot be
1
signed a priori as foods are both a source of arsenic and, through the nutrients they contain, an aid to
its metabolism.
5In our survey data, 26.4% of households obtain their water for cooking from sources other
than tube wells. Of these households, over three fourths treat the water, the majority by boiling and
the rest by an additive such as alum. As discussed below households using non-tubewell-sourced
cooking water spend significantly more time fetching water and fuel.
6
r :
ij
(4) h = (A , r ),
ij ij ij
where h <0.
1
In this set-up, the sources of exogenous variability in retained arsenic are e, which is locale-
j
specific, and ì , which is person-specific. Given these relationships, the association between
ij
measures of individual-specific retained arsenic A and, say, productivity h in a given environment
ij ij
(given water quality e and local prices p ) j is:
j jx
(5) dh /dA = h (1 + A ù(cid:78)(dt /dì )(dì /dA ) + A (dC /dF )(dF /dh )),
ij ij A 2j ij ij ij ij 1 ij ij ij ij
which reflects the association between observed individual arsenic and the exogenous and
endogenous components of arsenic retention. If variation in arsenic retention affects either the
composition of goods consumed or water purification effort the association between retained
arsenic and measures of capabilities will not identify the causal effect of arsenic retention. Similarly,
the association between measures of the quality of water consumed and A also reflects behavior,
ij
and not just the technological relationship given by (1):
(6) dA /dù = A + h wù(cid:78)dt /dw
ij ij 2 A j ij ij
The biases in the associations (5) and (6) - the terms associated with the behavioral response
to the exogenous components of arsenic (As) ingestion and retention - depend on how retained
arsenic affects behavior. But is it plausible that arsenic affects behavior if the costs and even the
amounts of arsenic ingested or retained are unobserved by agents and agents do not even know how
or whether that arsenic affects their productivity? We now show that under plausible information
assumptions changes in arsenic retention or in available water quality will affect water purification
effort. In particular, we assume that agents only have public health information: (i) they know that
arsenic is deleterious to health, (ii) they know which non-local water sources reduce arsenic ingestion
and (iii) they know how water purification effort affects arsenic retention. However, they are
uninformed about both (i) the effects of As on productivity h, (ii) their own individual arsenic
retention A and (iii) their own methylation efficiency endowment ì . They only observe their own
ij ij
productivity. The individual effects of arsenic retention are hidden.
For simplicity we ignore for now the effect of the consumption of specific foods on arsenic
ingestion and consider the consumption of only one aggregate consumption good C with a unit
ij
market price. Changes in effort are thus the only source of endogeneity in this case. The utility
function is
7
(7) U = U(A *, C ; u ) U <0, U >0, U >0, U <0,
ij ij ij 1 2 11 22
where A * = the agent’s beliefs about his retained arsenic and we have added an individual-specific
ij
preference parameter u .
ij
What is the optimal time spent fetching water, given the beliefs of agents? The FOC is:
(8) U A *ù(cid:78) = U w ,
A 2 C ij
Agents face a trade-off between consumption (good) and arsenic (bad) because effort reduces the
time available for income earning. Note that if agents were informed about the economic
consequences of arsenic retention the FOC is:
(9) U A *ù(cid:78) = U (w - A *ù(cid:78)h w(Ù - t ));
A 2 C ij 2 1 j ij
Comparing (8) to (9) indicates that lack of knowledge about the relationship between arsenic
retention and productivity in the population leads to higher levels of contaminated water
consumption (less water purification effort) than is optimal, if lowering arsenic retention actually
increases productivity, as U w > U (w - A *ù(cid:78)h w(Ù - t )). There is thus a productivity and health
C ij C ij 2 1 j ij
payoff to the dissemination of information about the economic consequences of arsenic retention, if
there are such consequences.
In this simple model, given realistic information constraints, the bias in (5) and (6), which
depends on how arsenic affects water purification effort, cannot be signed:
Proposition 1:
The effect of retained arsenic on the demand for improved water via increased purification effort is ambiguous.
The bias in the association between individual arsenic retention and productivity thus cannot be signed.
Proof: Assume for simplicity that A and C are separable in U, then
(10) dt /dì = h A [U w /Ö + dt /dF w (Ù - t )],
ij ij 1 3 C j ij j ij ij
where Ö = -[(A ù(cid:78))2 U + U A (ù(cid:78))2 + A ù(cid:79)] <0 and dt /dF = U /Ö > 0
2 AA A 22 2 ij j CC
The first term in (10) is negative - lower arsenic retention increases market productivity and the
opportunity cost of effort. The second term is the income effect, which is positive on purification
effort - the increase in income (or C) lowers the marginal utility of consumption relative to that of
arsenic.
Improvements of the local water source (change in e) also affect behavior, except that in
j
principle, agents can be told the level of e so that there is an additional negative public-health
j
information effect on efforts to reduce arsenic ingestion, which is embodied in the first term in
brackets in (11):
8